Abstract:

Differential privacy preserving is a privacy preserving method based on data distortion,which protects the sensitive data and keeps the data statistical properties by adding random noise.To protect data privacy for the clustering process of DBScan, we present a novel DP-DBScan clustering algorithm in the framework of differential privacy preserving.Subjected to the restriction on εdifferential privacy, the proposed DP-DBScan clustering algorithm can not only protect personal privacy effectively but can be applied to data sets of different sizes and dimensions.Experimental results show that,compared with the DBScan clustering method,the DP-DBScan clustering algorithm achieves clustering validity as well as differential privacy preserving when a small amount of noise are added.

Key words: differential privacy;DBScan;DP-DBScan;privacy preserving;data mining